SoroushVahidi/scidocs huggingface dataset:
Learning to Detect Anomalies in Surveillance Video
(2015)

Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization (SSMF) to learn local patterns at each pixel, and a Histogram of Nonnegative Coefficients (HNC) can be constructed as local feature which is more expressive than previously used features like Histogram of Oriented Gradients (HOG). At model level, we learn a probability model which takes the spatial and temporal contextual information into consideration. Our framework is totally unsupervised requiring no human-labeled training data. With more expressive features and more complicated model, our framework can accurately detect and localize anomalies in surveillance video. We carried out extensive experiments on several benchmark video datasets for anomaly detection, and the results demonstrate the superiority of our framework to state-of-the-art approaches, validating the effectiveness of our framework.
SAIL: single access point-based indoor localization
(2014)

This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.
Protective action of green tea catechins in neuronal mitochondria during aging.
(2015)

Mitochondria are central players in the regulation of cell homeostasis. They are essential for energy production but at the same time, reactive oxygen species accumulate as byproducts of the electron transport chain causing mitochondrial damage. In the central nervous system, senescence and neurodegeneration occur as a consequence of mitochondrial oxidative insults and impaired electron transfer. The accumulation of several oxidation products in neurons during aging prompts the idea that consumption of antioxidant compounds may delay neurodegenerative processes. Tea, one of the most consumed beverages in the world, presents benefits to human health that have been associated to its abundance in polyphenols, mainly catechins, that possess powerful antioxidant properties in vivo and in vitro. In this review, the focus will be placed on the effects of green tea catechins in neuronal mitochondria. Although these compounds reach the brain in small quantities, there are several possible targets, signaling pathways and molecular machinery impinging in the mitochondria that will be highlighted. Accumulated evidence thus far seems to indicate that catechins help prevent neurodegeneration and delay brain function decline.
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
(2015)

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to stateof-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.
Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments
(2011)

Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.
The dependency paradox in close relationships: accepting dependence promotes independence.
(2007)

Using multiple methods, this investigation tested the hypothesis that a close relationship partner's acceptance of dependence when needed (e.g., sensitive responsiveness to distress cues) is associated with less dependence, more autonomous functioning, and more self-sufficiency (as opposed to more dependence) on the part of the supported individual. In two studies, measures of acceptance of dependency needs and independent functioning were obtained through couple member reports, by observing couple members' behaviors during laboratory interactions, by observing responses to experimentally manipulated partner assistance provided during an individual laboratory task, and by following couples over a period of 6 months to examine independent goal striving as a function of prior assessments of dependency acceptance. Results provided converging evidence in support of the proposed hypothesis. Implications of the importance of close relationships for optimal individual functioning are discussed.
Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network
(2016)

Estimation of in-field biomass and crop composition is important for both farmers and researchers. Using close-up high resolution images of the crops, crop species can be distinguished using image processing. In the current study, deep convolutional neural networks for semantic segmentation (or pixel-wise classification) of cluttered classes in RGB images was explored in case of catch crops and volunteer barley cereal. The dataset consisted of RGB images from a plot trial using oil radish as catch crops in barley. The images were captured using a high-end consumer camera mounted on a tractor. The images were manually annotated in 7 classes: oil radish, barley, weed, stump, soil, equipment and unknown. Data argumentation was used to artificially increase the dataset by transposing and flipping the images. A modified version of VGG-16 deep neural network was used. First, the last fully-connected layers were converted to convolutional layer and the depth was modified to cope with our number of classes. Secondly, a deconvolutional layer with a 32 stride was added between the last fully-connected layer and the softmax classification layer to ensure that the output layer has the same size as the input. Preliminary results using this network show a pixel accuracy of 79% and a frequency weighted intersection over union of 66%. These preliminary results indicate great potential in deep convolutional networks for segmentation of plant species in cluttered RGB images.
Recent Research in Cooperative Control of Multivehicle
(2007)

This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus. DOI: 10.1115/1.2766721
"Green" electronics: biodegradable and biocompatible materials and devices for sustainable future.
(2014)

"Green" electronics represents not only a novel scientific term but also an emerging area of research aimed at identifying compounds of natural origin and establishing economically efficient routes for the production of synthetic materials that have applicability in environmentally safe (biodegradable) and/or biocompatible devices. The ultimate goal of this research is to create paths for the production of human- and environmentally friendly electronics in general and the integration of such electronic circuits with living tissue in particular. Researching into the emerging class of "green" electronics may help fulfill not only the original promise of organic electronics that is to deliver low-cost and energy efficient materials and devices but also achieve unimaginable functionalities for electronics, for example benign integration into life and environment. This Review will highlight recent research advancements in this emerging group of materials and their integration in unconventional organic electronic devices.
Stability of Causal Inference
(2016)

We consider the sensitivity of causal identi cation to small perturbations in the input. A long line of work culminating in papers by Shpitser and Pearl (2006) and Huang and Valtorta (2008) led to a complete procedure for the causal identi cation problem. In our main result in this paper, we show that the identi cation function computed by these procedures is in some cases extremely unstable numerically. Speci cally, the “condition number” of causal identi cation can be of the order of Ω(exp(n0.49)) on an identi able semi-Markovian model with n visible nodes. That is, in order to give an output accurate to d bits, the empirical probabilities of the observable events need to be obtained to accuracy d +Ω(n0.49) bits.
A Fuzzy Local Search Classifier for Intrusion Detection
(2011)

In this paper, we propose a fuzzy local search (FLS) method for intrusion detection. The FLS system is a fuzzy classifier, whose knowledge base is modeled as a fuzzy rule such as "if-then" and improved by a local search metaheuristic. The proposed method is implemented and tested on the benchmark KDD'99 intrusion dataset. The results are encouraging and demonstrate the benefits of our approach.
A Study on Watermarking Schemes for Image Authentication
(2010)

The digital revolution in digital image processing has made it possible to create, manipulate and transmit digital images in a simple and fast manner. The adverse affect of this is that the same image processing techniques can be used by hackers to tamper with any image and use it illegally. This has made digital image safety and integrity the top prioritized issue in today’s information explosion. Watermarking is a popular technique that is used for copyright protection and authentication. This paper presents an overview of the various concepts and research works in the field of image watermark authentication. In particular, the concept of content-based image watermarking is reviewed in details.
The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning
(2014)

Self-regulated learning is recognized as a critical factor for successful online learning, and students’ perceived academic control and academic emotions are important antecedents of self-regulated learning. Because emotions and cognition are interrelated, investigating the joint relationship between perceived academic control and academic emotions on self-regulated learning would be valuable to understanding the process of self-regulated learning. Therefore, this study examined the role of academic emotions (enjoyment, anxiety, and boredom) in the relationship between perceived academic control and selfregulated learning in online learning. The path model was proposed to test the mediating and moderating effects of academic emotions. Data were collected from 426 Korean college students registered in online courses, and a path analysis was conducted. The results demonstrated that enjoyment mediated the relationship between perceived academic control and self-regulated learning, but the moderating effect of enjoyment was not significant. Boredom and anxiety did not have significant mediating effects on self-regulated learning, whereas they showed significant moderating effects in the relationship between perceived academic control and self-regulated learning. The role of academic emotions in learning and their implications for facilitating students’ self-regulated learning in online learning were discussed based on the findings. 2014 Elsevier Ltd. All rights reserved.
IoT based smart emergency response system for fire hazards
(2017)

The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use those assets. IoT mitigates many of the challenges to emergency response including present problems like a weak communication network and information lag. In this paper it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Espressif wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.
Machinery equipment early fault detection using Artificial Neural Network based Autoencoder
(2017)

Machinery equipment early fault detection is still in an open challenge. The objective of this paper is to introduce a parametric method Artificial Neural Network based Autoencoder implemented to perform early fault detection of a machinery equipment. The performance of this method is then compared to one of the industry state of the art nonparametric methods called Similarity Based Modeling. The comparison is done by analyzing the implementation result on both artificial and real case dataset. Root Mean Square Error (RMSE) is applied to measure the performance. Based on the result of the research, both of these methods are effective to do pattern recognition and able to identify data anomaly or in this case is fault identification.
Anomaly Detection in Video Surveillance via Gaussian Process
(2015)

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze di®erent anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce
Forensic Analysis and Anonymisation of Printed Documents
(2018)

Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.
AtDELFI: automatically designing legible, full instructions for games
(2018)

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (Automatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
(2001)

Categorization of documents is challenging, as the number o f discriminating words can be very large. We present a nearest neighbor classification scheme for text categoriz ation in which the importance of discriminating words is learned using mutual information and weight adjustment tec hniques. The nearest neighbors for a particular document are then computed based on the matching words and their weigh ts. We evaluate our scheme on both synthetic and real world documents. Our experiments with synthetic data sets s how that this scheme is robust under different emulated conditions. Empirical results on real world documents demo nstrate that this scheme outperforms state of the art classification algorithms such as C4.5, RIPPER, Rainbow, an d PEBLS.
Multiple HOG templates for gait recognition
(2012)

In gait recognition field, template-based approaches such as Gait Energy Image (GEI) and Chrono-Gait Image (CGI) can achieve good recognition performance with low computational cost. Meanwhile, CGI can preserve temporal information better than GEI. However, they pay less attention to the local shape features. To preserve temporal information and generate more abundant local shape features, we generate multiple HOG templates by extracting Histogram of Oriented Gradients (HOG) of GEI and CGI templates. Experiments show that compared with several published approaches, our proposed multiple HOG templates achieve better performance for gait recognition.
Improving predictive inference under covariate shift by weighting the log-likelihood function
(2000)

A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function. This approach is e ective in cases such as sample surveys or design of experiments, where the observed covariate follows a di erent distribution than that in the whole population. Under misspeci cation of the parametric model, the optimal choice of the weight function is asymptotically shown to be the ratio of the density function of the covariate in the population to that in the observations. This is the pseudo-maximum likelihood estimation of sample surveys. The optimality is de ned by the expected Kullback–Leibler loss, and the optimal weight is obtained by considering the importance sampling identity. Under correct speci cation of the model, however, the ordinary maximum likelihood estimate (i.e. the uniform weight) is shown to be optimal asymptotically. For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss. The method is also applied to a weighted version of the Bayesian predictive density. Numerical examples as well as Monte-Carlo simulations are shown for polynomial regression. A connection with the robust parametric estimation is discussed. c © 2000 Elsevier Science B.V. All rights reserved.
Discriminative learning for differing training and test distributions
(2007)

We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.